The field of medical imaging has seen significant advances since the time X-Rays were first used to determine anatomical abnormalities. Medical imaging hardware has progressed in the form of newer machines such as Medical Resonance Imaging (MRI) scanners, Computed Axial Tomography (CAT) scanners, etc. Because of large amount of image data generated by such modern medical scanners, there has been and remains a need for developing image processing techniques that can automate some or all of the processes to determine the presence of anatomical abnormalities in scanned medical images.
Recognizing anatomical structures within digitized medical images presents multiple challenges. For example, a first concern relates to the accuracy of recognition of anatomical structures within an image. A second area of concern is the speed of recognition. Because medical images are an aid for a doctor to diagnose a disease or condition, the speed with which an image can be processed and structures within that image recognized can be of the utmost importance to the doctor reaching an early diagnosis. Hence, there is a need for improving recognition techniques that provide accurate and fast recognition of anatomical structures and possible abnormalities in medical images.
Digital medical images are constructed using raw image data obtained from a scanner, for example, a CAT scanner, MRI, etc. Digital medical images are typically either a two-dimensional (“2-D”) image made of pixel elements or a three-dimensional (“3-D”) image made of volume elements (“voxels”). Such 2-D or 3-D images are processed using medical image recognition techniques to determine the presence of anatomical structures such as cysts, tumors, polyps, etc. Given the amount of image data generated by any given image scan; it is preferable that an automatic technique should point out anatomical features in the selected regions of an image to a doctor for further diagnosis of any disease or condition.
One general method of automatic image processing employs feature based recognition techniques to determine the presence of anatomical structures in medical images. However, feature based recognition techniques can suffer from accuracy problems.
Automatic image processing and recognition of structures within a medical image is generally referred to as Computer-Aided Detection (CAD). A CAD system can process medical images and identify anatomical structures including possible abnormalities for further review. Such possible abnormalities are often called candidates and are considered to be generated by the CAD system based upon the medical images.
One particular area in which the use of CAD systems would be highly advantageous is in the imaging of the spinal column. A precise vertebra segmentation and identification method is in high demand due to its importance to, and impact on, many orthopedic, neurological, and ontological applications. Unfortunately, the task of segmenting and identifying vertebrae, even using CAD systems that are well-developed for other anatomical structures, often proves inaccurate and therefore inadequate. The difficulty lies in the inherent complexity of vertebrae. Each vertebra can be modeled against the same vertebra in other patients and an average, or mean vertebra model can be created. However, the variation within the same class of vertebra as well as the variation in neighboring structures makes vertebral modeling and imaging extremely difficult.
Several methods have been reported addressing segmentation and/or identification of vertebra using a wide variety of different imaging modalities (e.g., magnetic resonance imaging (MRI), computed tomography (CT), etc.). Such prior approaches include a method to automatically extract and partition the spinal cord in CT images as described in Yao, J., O'Conner, S., Summers, R.: Automated Spinal Column Extraction and Partitioning. In: Proc. of IEEE ISM, pp. 390-393 (2006), which is hereby incorporated by reference herein. Another prior approach includes using a surface-based registration for automatic lumbar vertebra identification as described in Herring, J., Dawant, B.: Automatic Lumbar Vertebral Identification Using Surface-Based Registration. Computers and Biomedical Research 34(2), 629-642 (2001), which is hereby incorporated by reference in its entirety.
More recent approaches propose a model-based solution for vertebral detection, segmentation, and identification in CT images, as described, for example, in Klinder, T., Ostermatm, J., Ehm, M., Franz, A., Kneser, R., Lorenz, C.: Automated Model-Based Vertebra Detection, Identification, and Segmentation in CT Images. Medical Image Analysis 13, 471-481 (2009), which is hereby incorporated by reference herein. The approach described in Klinder achieved competitive identification rates of approximately 70% when identifying a single vertebra and 100% when identifying 16 or more vertebrae. However, that identification algorithm is based on vertebral appearance model (i.e., average volume block) spatial registration and matching which is extremely computationally consuming. In order to achieve the high identification rates of the Klinder approach requires approximately 20-30 minutes of computational time. In a real-world hospital setting, such system dedication to a single patient's data is not practical or realistic.
Therefore there is a need for systems and methods for precise segmentation and identification of vertebrae that is both accurate and efficient.